def load_variables()

in utils.py [0:0]

• 33 lines of code
• 11 McCabe index (conditional complexity)

def load_variables(sess, weights, ignore=None, trainable=False, ema=True):
    '''ema refers to whether the exponential moving averaged weights are used to
    initialize the true weights or not.'''
    weights = {os.path.normpath(key): value for key, value in weights.items()}
    ops = []
    feed_dict = {}
    if ema:
        gvs_map = {v.name: v for v in tf.global_variables()}
    for i, var in enumerate(get_variables(trainable=trainable)):
        var_name = os.path.normpath(var.name)
        if ignore:
            do_not_load = False
            for ignore_substr in ignore:
                if ignore_substr in var_name:
                    do_not_load = True
            if do_not_load:
                continue
        ph = tf.placeholder(dtype=var.dtype, shape=var.shape)
        ops.append(var.assign(ph))
        if ema:
            ema_name = f'{var_name[:-2]}/Ema/ema:0'
            # We assign the EMA value to the current value
            try:
                feed_dict[ph] = weights[ema_name]
            except KeyError:
                print(f'warning: ema var not found for {var_name}')
                feed_dict[ph] = weights[var_name]
            # We also assign the EMA value to the current EMA, which will otherwise
            # use the initialized value of the variable (random)
            ema_var = gvs_map[ema_name]
            ph = tf.placeholder(dtype=ema_var.dtype, shape=ema_var.shape)
            ops.append(ema_var.assign(ph))
            feed_dict[ph] = weights[ema_name]
        else:
            feed_dict[ph] = weights[var_name]
    sess.run(ops, feed_dict)